{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "30af8cfd", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:14:59.153807Z", "iopub.status.busy": "2024-10-11T06:14:59.153361Z", "iopub.status.idle": "2024-10-11T06:14:59.158878Z", "shell.execute_reply": "2024-10-11T06:14:59.158179Z" }, "nbsphinx": "hidden" }, "outputs": [], "source": [ "# Copyright 2024 Keysight Technologies Inc." ] }, { "cell_type": "raw", "id": "4b879ca2", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "\n", ".. _res_spec:\n", "\n", "Resonator Spectroscopy\n", "======================\n", "Quantum computers use qudits for storing and processing information and rely on\n", "readout resonators for measurement. These resonators are coupled to qudits and help\n", "determine their state by detecting changes in the electromagnetic field.\n", "\n", "Resonator spectroscopy is used to characterize the resonators involved in qudit state\n", "readout. It focuses on finding the resonance frequency of the readout resonator, which\n", "is designed to be a specific value but often deviates due to manufacturing variations.\n", "Knowing this frequency is crucial and is typically the first step in characterizing a\n", "qubit chip.\n", "\n", "A resonator spectroscopy experiment typically follows these steps:\n", "\n", " #. Initialize the qudit: Prepare the qudit in a known state, usually the ground\n", " state.\n", "\n", " #. Sweep a probe signal: Apply a probe signal at varying frequencies through the\n", " resonator coupled to the qudit.\n", "\n", " #. Measure the response: Observe how the resonator responds by checking the\n", " transmission or reflection of the probe signal.\n", "\n", " #. Identify the resonance frequency: Find the frequency that gives the strongest\n", " response, which is the resonance frequency of the readout resonator.\n", "\n", "The strongest response is identified by a characteristic peak or dip (depending on\n", "the measurement setup) in the transmission or reflection curve as the frequency\n", "changes, indicating the resonant frequency of the resonator.\n", "\n", "Let's walk through the steps to perform this experiment on Keysight hardware using\n", "the QCS package.\n", "\n", ".. note::\n", "\n", " The assets are set up to map a maximum of 4 qudits to a single physical AWG and\n", " digitizer channel with an LO frequency of 5.5 GHz. For the purposes of this\n", " demonstration, we connect the output of the AWG to the digitizer so we can capture\n", " both the control and readout pulses.\n", "\n", "First, we'll load all the necessary packages and a channel mapper, which links up to\n", "the hardware channels. If a channel mapper doesn't exist, we can create a new one and\n", "add the hardware references to it. Then we generate a calibration set for the qubits\n", "using :py:func:`~keysight.qcs.experiments.make_calibration_set`. This file that stores\n", "all the important variables and parameters we need to run experiments on our quantum\n", "system." ] }, { "cell_type": "code", "execution_count": 2, "id": "1f8bf7e9", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:14:59.162831Z", "iopub.status.busy": "2024-10-11T06:14:59.162264Z", "iopub.status.idle": "2024-10-11T06:15:02.560480Z", "shell.execute_reply": "2024-10-11T06:15:02.559494Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import keysight.qcs as qcs\n", "from keysight.qcs.experiments import make_calibration_set\n", "from keysight.qcs.experiments import ResonatorSpectroscopy, ResonatorSpectroscopy2D\n", "from simulated_experiments.simulated_experiments import (\n", " SimulatedResSpectroscopyExperiment,\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "id": "ba73a072", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:02.565530Z", "iopub.status.busy": "2024-10-11T06:15:02.565045Z", "iopub.status.idle": "2024-10-11T06:15:02.753670Z", "shell.execute_reply": "2024-10-11T06:15:02.752802Z" } }, "outputs": [], "source": [ "# Set this to True if channel mapper exists\n", "channel_mapper_exists = False\n", "\n", "if channel_mapper_exists:\n", " mapper = qcs.load(\"\")\n", "else:\n", " # generate an empty channel mapper with the correct address\n", " mapper = qcs.ChannelMapper(\"\")\n", "\n", "# Generate the calibration_set template\n", "calibration_set = make_calibration_set(qubits=1)\n", "\n", "# Set this to True if connected to hardware\n", "run_on_hw = False" ] }, { "cell_type": "raw", "id": "0ed1c983", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Next, we’ll set up our ``ResonatorSpectroscopy`` experiment to find the readout\n", "resonator's frequency. The :py:class:`~keysight.qcs.experiments.ResonatorSpectroscopy`\n", "class is actually a subclass of the more general\n", ":py:class:`~keysight.qcs.experiments.CalibrationExperiment`. The\n", "``CalibrationExperiment`` class is specifically designed to calibrate quantum\n", "operations for individual qubits and store all the parameters in the\n", "``calibration_set``.\n", "\n", "In this example, we're setting up the experiment to calibrate the readout resonator\n", "for 1 qubit." ] }, { "cell_type": "code", "execution_count": 4, "id": "a3b86ef6", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:02.757480Z", "iopub.status.busy": "2024-10-11T06:15:02.757128Z", "iopub.status.idle": "2024-10-11T06:15:03.094382Z", "shell.execute_reply": "2024-10-11T06:15:03.093421Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "# Define the number of qubits\n", "n_qubits = 1\n", "qubits = qcs.Qudits(range(n_qubits))\n", "\n", "# Create a resonator spectroscopy experiment\n", "res_spectroscopy = ResonatorSpectroscopy(\n", " backend=mapper,\n", " calibration_set=calibration_set,\n", " qubits=qubits,\n", " operation=\"measurement\",\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "a9eb8181", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:03.098173Z", "iopub.status.busy": "2024-10-11T06:15:03.097824Z", "iopub.status.idle": "2024-10-11T06:15:03.176134Z", "shell.execute_reply": "2024-10-11T06:15:03.175439Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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The frequency range can always be configured or\n", "modified using the ``configure_repetitions`` method.\n", "\n", ".. note::\n", " If the \"measurement\" operation is stored in the ``calibration_set`` under a\n", " different name, you’ll need to pass that name as an argument (``operation=``)\n", " to the ``ResonatorSpectroscopy`` class so it can reference the correct entry in the\n", " ``calibration_set``." ] }, { "cell_type": "code", "execution_count": 6, "id": "11bdc283", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:03.180091Z", "iopub.status.busy": "2024-10-11T06:15:03.179694Z", "iopub.status.idle": "2024-10-11T06:15:03.186547Z", "shell.execute_reply": "2024-10-11T06:15:03.185813Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "# Retrieve the readout frequencies stored in the calibration set\n", "current_freq = res_spectroscopy.calibration_set.variables.readout_frequencies[\n", " list(qubits.labels)\n", "].value\n", "\n", "# Set the frequency range for sweeping\n", "start_frequency = current_freq - 200e6\n", "end_frequency = current_freq + 200e6\n", "steps = 9\n", "freq_scan_values = np.linspace(start_frequency, end_frequency, steps)" ] }, { "cell_type": "code", "execution_count": 7, "id": "0ebcbbb6", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:03.189796Z", "iopub.status.busy": "2024-10-11T06:15:03.189477Z", "iopub.status.idle": "2024-10-11T06:15:03.205219Z", "shell.execute_reply": "2024-10-11T06:15:03.204360Z" } }, "outputs": [], "source": [ "# Configure the repetitions for this experiment\n", "res_spectroscopy.configure_repetitions(\n", " frequencies=freq_scan_values, n_shots=1, frequency_name=\"readout_frequencies\"\n", ")" ] }, { "cell_type": "raw", "id": "af9878c6", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "We’ll now compile the program down to the waveform level and use the ``render`` method\n", "to visualize the waveforms to be executed on the hardware. You can adjust the index\n", "parameter to view individual waveforms from the sweep.\n", "\n", ".. note::\n", " ``res_spectroscopy.render()`` can also be used to display the waveforms. This\n", " requires a properly defined channel mapper with all connections configured and\n", " passed into the experiment class. For more information, refer to\n", " :py:meth:`~keysight.qcs.experiments.Experiment.render`.\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "31d8e740", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:03.209072Z", "iopub.status.busy": "2024-10-11T06:15:03.208727Z", "iopub.status.idle": "2024-10-11T06:15:03.740180Z", "shell.execute_reply": "2024-10-11T06:15:03.739436Z" } }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "dx": 2e-10, "legendgroup": "readout_pulse, labels: (0,)", "legendgrouptitle": { "text": "readout_pulse, labels: (0,)" }, "name": "Drive pulse for readout_pulse, labels: (0,)", "type": "scatter", "x0": 0, "xaxis": "x", "y": [ 0.0003127645009414709, 0.0009050420777136429, 0.001399532220232912, 0.0017375015738507083, 0.0018695661244606256, 0.001758770740034472, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# View the waveforms to be executed on the hardware\n", "res_spectroscopy.compiled_program.render(\n", " channel_subplots=False, lo_frequency=5e9, sweep_index=4, sample_rate=5e9\n", ")" ] }, { "cell_type": "raw", "id": "c7dbcff0", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "To execute this experiment, we can simply run" ] }, { "cell_type": "code", "execution_count": 9, "id": "c4579263", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:03.777473Z", "iopub.status.busy": "2024-10-11T06:15:03.777065Z", "iopub.status.idle": "2024-10-11T06:15:03.892498Z", "shell.execute_reply": "2024-10-11T06:15:03.891603Z" } }, "outputs": [], "source": [ "if run_on_hw:\n", " res_spectroscopy.execute()\n", "else:\n", " # load in a previously executed version of this experiment\n", " res_spectroscopy = qcs.load(\"ReadoutSpectroscopy.qcs\")" ] }, { "cell_type": "raw", "id": "05ddc783", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "We can now plot the output trace waveforms that have been generated by the AWGs. To\n", "plot a specific channel, you may pass the argument to the ``channels`` parameter of\n", "``plot_trace`` method\n", "\n", ".. note::\n", " The ``plot_iq`` method is ideal for visualizing the transmission/reflection response\n", " from the resonator" ] }, { "cell_type": "code", "execution_count": 10, "id": "4912823c", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:03.896366Z", "iopub.status.busy": "2024-10-11T06:15:03.895996Z", "iopub.status.idle": "2024-10-11T06:15:04.147380Z", "shell.execute_reply": "2024-10-11T06:15:04.146643Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "dx": 2.0833333333333334e-10, "legendgroup": "readout_acquisition, labels: (0,)", "legendgrouptitle": { "text": "readout_acquisition, labels: (0,)" }, "name": "Re(V) for (readout_frequencies, 4.95 GHz)", "type": "scatter", "x0": 0, "xaxis": "x", "y": [ -0.003076171735301614, 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"Rockwell", "size": 14 } }, "hovermode": "x unified", "legend": { "bgcolor": "white", "bordercolor": "Black", "borderwidth": 1, "font": { "family": "Rockwell" } }, "xaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true }, "yaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true } } }, "width": 900, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ], "showticklabels": true, "title": { "text": "Time (s)" } }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ], "title": { "text": "Fractional Voltage" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the trace waveforms\n", "res_spectroscopy.plot_trace()" ] }, { "cell_type": "raw", "id": "6623ab2e", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "Fitting and calibration workflow\n", "\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", "Now that we've plotted the response, let's go over how to extract the resonator's\n", "resonance frequency and update the variables in the calibration set.\n", "\n", "For this example, we will load an experiment with simulated data:\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "1c235106", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.157533Z", "iopub.status.busy": "2024-10-11T06:15:04.157181Z", "iopub.status.idle": "2024-10-11T06:15:04.185834Z", "shell.execute_reply": "2024-10-11T06:15:04.184971Z" } }, "outputs": [], "source": [ "res_spectroscopy = SimulatedResSpectroscopyExperiment(calibration_set, qubits)" ] }, { "cell_type": "code", "execution_count": 12, "id": "38402ae8", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.189547Z", "iopub.status.busy": "2024-10-11T06:15:04.189228Z", "iopub.status.idle": "2024-10-11T06:15:04.372051Z", "shell.execute_reply": "2024-10-11T06:15:04.371130Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "IQ Value: %{y}
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}, "autotypenumbers": "strict", "hoverlabel": { "bgcolor": "white", "font": { "family": "Rockwell", "size": 14 } }, "hovermode": "x unified", "legend": { "bgcolor": "white", "bordercolor": "Black", "borderwidth": 1, "font": { "family": "Rockwell" } }, "xaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true }, "yaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true } } }, "width": 900, "xaxis": { "anchor": "y", "automargin": true, "domain": [ 0.0, 1.0 ], "nticks": 12, "showticklabels": true, "tickangle": 12, "title": { "text": "readout_frequencies" } }, "yaxis": { "anchor": "x", "automargin": true, "domain": [ 0.0, 1.0 ], "nticks": 12, "title": { "text": "I/Q Data" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res_spectroscopy.plot_iq(plot_type=\"linear\")" ] }, { "cell_type": "raw", "id": "20d42b7c", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The :py:meth:`~keysight.qcs.experiments.Experiment.fit` method takes the I/Q data\n", "and fits it to a complex lorentzian model, as specified by the built-in\n", ":py:class:`~keysight.qcs.analysis.ComplexLorentzian`.\n", "\n", "The result of the fit is an :py:class:`~keysight.qcs.analysis.EstimateCollection`,\n", "which contains individual :py:class:`~keysight.qcs.analysis.Estimate`\\ for each\n", "qubit's readout resonator that was fitted." ] }, { "cell_type": "code", "execution_count": 13, "id": "6388edaa", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.376764Z", "iopub.status.busy": "2024-10-11T06:15:04.376193Z", "iopub.status.idle": "2024-10-11T06:15:04.420506Z", "shell.execute_reply": "2024-10-11T06:15:04.419530Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimate(amplitude=0.000499314294551207, resonance=5149864062.317487, kappa=19867396.825222116, offset_real=-0.02699828492003239, ...)\n" ] } ], "source": [ "ec = res_spectroscopy.fit()\n", "\n", "# View the data format of the estimate\n", "print(ec.estimates[0])" ] }, { "cell_type": "raw", "id": "62ffa80c", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "The fitted and the pre-processed data can be visualized by calling the\n", ":py:meth:`~keysight.qcs.experiments.Experiment.plot` method:" ] }, { "cell_type": "code", "execution_count": 14, "id": "bd294b45", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.424915Z", "iopub.status.busy": "2024-10-11T06:15:04.424342Z", "iopub.status.idle": "2024-10-11T06:15:04.728221Z", "shell.execute_reply": "2024-10-11T06:15:04.727218Z" } }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res_spectroscopy.plot()" ] }, { "cell_type": "raw", "id": "9673431f", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "To compute the new calibration values for the readout resonator, use the\n", ":py:meth:`~keysight.qcs.experiments.ResonatorSpectroscopy.get_updated_calibration_values`\n", "method. This will calculate the updated calibration values based on the fit results." ] }, { "cell_type": "code", "execution_count": 15, "id": "f0caf071", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.733050Z", "iopub.status.busy": "2024-10-11T06:15:04.732461Z", "iopub.status.idle": "2024-10-11T06:15:04.774495Z", "shell.execute_reply": "2024-10-11T06:15:04.773726Z" } }, "outputs": [ { "data": { "text/plain": [ "{'readout_frequencies': 5149864062.317487}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_spectroscopy.get_updated_calibration_values()" ] }, { "cell_type": "raw", "id": "864b4c34", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Let's check the current value of the variable in the calibration_set" ] }, { "cell_type": "code", "execution_count": 16, "id": "3e73e028", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.778410Z", "iopub.status.busy": "2024-10-11T06:15:04.778017Z", "iopub.status.idle": "2024-10-11T06:15:04.785547Z", "shell.execute_reply": "2024-10-11T06:15:04.784784Z" } }, "outputs": [ { "data": { "text/plain": [ "array([5.15e+09])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_spectroscopy.calibration_set.variables.readout_frequencies.value" ] }, { "cell_type": "raw", "id": "d18c7a1a", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "We update this variable as follows:" ] }, { "cell_type": "code", "execution_count": 17, "id": "84bc0221", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.789031Z", "iopub.status.busy": "2024-10-11T06:15:04.788671Z", "iopub.status.idle": "2024-10-11T06:15:04.834350Z", "shell.execute_reply": "2024-10-11T06:15:04.833562Z" } }, "outputs": [ { "data": { "text/plain": [ "array([5.14986406e+09])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_spectroscopy.set_updated_calibration_values()\n", "\n", "# Print the value of the variable to confirm the update\n", "res_spectroscopy.calibration_set.variables.readout_frequencies.value" ] }, { "cell_type": "raw", "id": "3eb56757", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "Resonator Spectroscopy vs Power\n", "-------------------------------\n", "To fully understand and characterize the coupling between a readout resonator and a\n", "qubit, it's important to perform a 2D sweep, where both the input power and the\n", "frequency of the readout pulse are varied. The 2D sweep is particularly useful because\n", "it reveals how the resonance frequency shifts with different power levels, allowing us\n", "to identify any nonlinearities in the system. This helps determine the optimal\n", "setting for qubit readout.\n", "\n", "Running the ``ResonatorSpectroscopy2D`` experiment is similar to the previous example.\n", "First, we define the qubits we want to target, then we initialize the\n", "``ResonatorSpectroscopy2D`` experiment and draw the program.\n", "\n", ".. note::\n", " The qubits object is defined differently here to demonstrate an alternative\n", " approach. Notice how it affects the definition of other parameters in the\n", " experiment." ] }, { "cell_type": "code", "execution_count": 18, "id": "cb99210a", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.838091Z", "iopub.status.busy": "2024-10-11T06:15:04.837700Z", "iopub.status.idle": "2024-10-11T06:15:04.850643Z", "shell.execute_reply": "2024-10-11T06:15:04.849865Z" } }, "outputs": [], "source": [ "# Define the number of qubits and the calibration set\n", "n_qubits = [0, 1]\n", "qubits = qcs.Qudits(n_qubits)\n", "calibration_set = make_calibration_set(qubits=len(qubits))" ] }, { "cell_type": "code", "execution_count": 19, "id": "00577cdf", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.854082Z", "iopub.status.busy": "2024-10-11T06:15:04.853776Z", "iopub.status.idle": "2024-10-11T06:15:04.877684Z", "shell.execute_reply": "2024-10-11T06:15:04.876827Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "# Create a resonator spectroscopy experiment\n", "res_spectroscopy2D = ResonatorSpectroscopy2D(\n", " backend=mapper,\n", " calibration_set=calibration_set,\n", " qubits=qubits,\n", " operation=\"measurement\",\n", ")" ] }, { "cell_type": "code", "execution_count": 20, "id": "40425deb", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.881875Z", "iopub.status.busy": "2024-10-11T06:15:04.881513Z", "iopub.status.idle": "2024-10-11T06:15:04.905450Z", "shell.execute_reply": "2024-10-11T06:15:04.904742Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DurationArray(name=_implicit, shape=(2,), dtype=float, unit=s, value=[100 ns, 100 ns])
Integration Filter\n", " \n", " \n", "\n", "\n", "\n", "
\n", " RFWaveform \n", "
\n", "
\n", " Parameters\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DurationArraySlice(name=acquisition_duration, shape=(2,), dtype=float, unit=s, value=[100 ns, 100 ns])
AmplitudeArraySlice(name=measurement_integrator_amplitude, shape=(2,), dtype=float, unit=none, value=[1, 1])
FrequencyArraySlice(name=readout_frequencies, shape=(2,), dtype=float, unit=Hz, value=[5.15 GHz, 5.15 GHz])
EnvelopeConstantEnvelope()
Instantaneous PhaseArraySlice(name=measurement_integrator_phase, shape=(2,), dtype=float, unit=rad, value=[0 rad, 0 rad])
Post-phaseArraySlice(name=measurement_integrator_post_phase, shape=(2,), dtype=float, unit=rad, value=[0 rad, 0 rad])
\n", "
\n", "\n", "
ClassifierClassifier(ArraySlice(name=references, shape=(2, 2), dtype=complex, unit=none))
\n", "
\n", "\n", "\n", "
\n", "
\n", " 		\n", " \n", "
\n", " \n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
\n", " Delay on ('readout_acquisition', 0)\n", "
\n", "
\n", " Parameters\n", "
\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "
DurationScalar(name=_implicit, value=0 s, dtype=float, unit=s)
\n", "
\n", "\n", "\n", "
\n", "
\n", "
\n", " 1\n", " \n", " \n", "
\n", " \n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
\n", " Acquisition on ('readout_acquisition', 1)\n", "
\n", "
\n", " Parameters\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DurationArray(name=_implicit, shape=(2,), dtype=float, unit=s, value=[100 ns, 100 ns])
Integration Filter\n", " \n", " \n", "\n", "\n", "\n", "
\n", " RFWaveform \n", "
\n", "
\n", " Parameters\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DurationArraySlice(name=acquisition_duration, shape=(2,), dtype=float, unit=s, value=[100 ns, 100 ns])
AmplitudeArraySlice(name=measurement_integrator_amplitude, shape=(2,), dtype=float, unit=none, value=[1, 1])
FrequencyArraySlice(name=readout_frequencies, shape=(2,), dtype=float, unit=Hz, value=[5.15 GHz, 5.15 GHz])
EnvelopeConstantEnvelope()
Instantaneous PhaseArraySlice(name=measurement_integrator_phase, shape=(2,), dtype=float, unit=rad, value=[0 rad, 0 rad])
Post-phaseArraySlice(name=measurement_integrator_post_phase, shape=(2,), dtype=float, unit=rad, value=[0 rad, 0 rad])
\n", "
\n", "\n", "
ClassifierClassifier(ArraySlice(name=references, shape=(2, 2), dtype=complex, unit=none))
\n", "
\n", "\n", "\n", "
\n", "
\n", " 		\n", " \n", "
\n", " \n", "\n", "\n", "
\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
\n", " Delay on ('readout_acquisition', 1)\n", "
\n", "
\n", " Parameters\n", "
\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "
DurationScalar(name=_implicit, value=0 s, dtype=float, unit=s)
\n", "
\n", "\n", "\n", "
\n", "
\n", "
\n", "\n", "\n", "\n", "\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": { "text/html": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "# Draw the program to view the operations to be performed on hardware\n", "res_spectroscopy2D.draw()" ] }, { "cell_type": "raw", "id": "65e765ec", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "The experiment remains mostly the same, but now we’re also sweeping the amplitude\n", "parameter in the readout pulse. Next, we define the sweep values for both the\n", "amplitude and frequency of the readout pulse.\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "34def974", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.909453Z", "iopub.status.busy": "2024-10-11T06:15:04.909088Z", "iopub.status.idle": "2024-10-11T06:15:04.915700Z", "shell.execute_reply": "2024-10-11T06:15:04.914714Z" } }, "outputs": [], "source": [ "# Retrieve the readout frequencies stored in the calibration set for the target qubits\n", "current_freq = res_spectroscopy2D.calibration_set.variables.readout_frequencies[\n", " [0, 1]\n", "].value\n", "\n", "# Set the range for sweeping the readout pulse frequency\n", "start_frequency = current_freq - 200e6\n", "end_frequency = current_freq + 200e6\n", "freq_steps = 9\n", "freq_scan_values = np.linspace(start_frequency, end_frequency, freq_steps)" ] }, { "cell_type": "code", "execution_count": 22, "id": "7aeba7f1", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.919212Z", "iopub.status.busy": "2024-10-11T06:15:04.918868Z", "iopub.status.idle": "2024-10-11T06:15:04.924089Z", "shell.execute_reply": "2024-10-11T06:15:04.923293Z" } }, "outputs": [], "source": [ "# Set the range for sweeping the readout pulse amplitude (units = V)\n", "start_amplitude = 0.1 if len(qubits) == 1 else [0.1] * len(qubits)\n", "end_amplitude = 1 if len(qubits) == 1 else [1] * len(qubits)\n", "ampl_steps = 10\n", "ampl_scan_values = np.linspace(start_amplitude, end_amplitude, ampl_steps)" ] }, { "cell_type": "raw", "id": "91701bc0", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ ".. note::\n", " The arrays for amplitudes and frequencies can be defined in different ways, as\n", " demonstrated above. However, the shape of the arrays must match the number of\n", " qubits, or an ``InconsistentShapes`` error will occur.\n" ] }, { "cell_type": "code", "execution_count": 23, "id": "adc93850", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.927430Z", "iopub.status.busy": "2024-10-11T06:15:04.926915Z", "iopub.status.idle": "2024-10-11T06:15:04.940803Z", "shell.execute_reply": "2024-10-11T06:15:04.939867Z" } }, "outputs": [], "source": [ "# Configure the repetitions for this experiment\n", "res_spectroscopy2D.configure_repetitions(\n", " frequencies=freq_scan_values,\n", " frequency_name=\"readout_frequencies\",\n", " amplitudes=ampl_scan_values,\n", " amplitude_name=\"readout_pulse_amplitudes\",\n", " n_shots=1,\n", ")" ] }, { "cell_type": "raw", "id": "d54e079a", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "After configuring the repetitions, we compile the program to the waveform level and\n", "use the ``render`` method to visualize the waveforms. In this example, the\n", "``sweep_index`` is set to the midpoint of the nested sweeps. The right index\n", "represents the inner sweep (amplitude), and the left index represents the outer sweep\n", "(frequency)." ] }, { "cell_type": "code", "execution_count": 24, "id": "3d3a9d84", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:04.944438Z", "iopub.status.busy": "2024-10-11T06:15:04.944104Z", "iopub.status.idle": "2024-10-11T06:15:05.082961Z", "shell.execute_reply": "2024-10-11T06:15:05.082251Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "dx": 2e-10, "legendgroup": "readout_pulse, labels: (0,)", "legendgrouptitle": { "text": "readout_pulse, labels: (0,)" }, "name": "Drive pulse for readout_pulse, labels: (0,)", "type": "scatter", "x0": 0, "xaxis": "x", "y": [ 0.0018700890781240228, 0.0052576843218815955, 0.007624491984781647, 0.008409933504581439, 0.007222213134954023, 0.003884486760317131, -0.001538217733665497, -0.008734016288379222, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# View the waveforms to be executed on the hardware\n", "res_spectroscopy2D.compiled_program.render(\n", " channel_subplots=False, lo_frequency=5e9, sweep_index=(5, 5), sample_rate=5e9\n", ")" ] }, { "cell_type": "raw", "id": "2d10590c", "metadata": { "lines_to_next_cell": 0, "raw_mimetype": "text/restructuredtext" }, "source": [ "We now proceed to execute the experiment (or load a previous run) and plot the trace\n", "waveforms associated with a specific readout resonator. These waveforms show the\n", "output that has passed through the resonator." ] }, { "cell_type": "code", "execution_count": 25, "id": "55ce8bb6", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:05.088750Z", "iopub.status.busy": "2024-10-11T06:15:05.088423Z", "iopub.status.idle": "2024-10-11T06:15:05.129030Z", "shell.execute_reply": "2024-10-11T06:15:05.128034Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "if run_on_hw:\n", " res_spectroscopy2D.execute()\n", "else:\n", " # load in a previously executed version of this experiment\n", " res_spectroscopy2D = qcs.load(\"ReadoutSpectroscopy2D.qcs\")" ] }, { "cell_type": "code", "execution_count": 26, "id": "8df0b987", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:05.133488Z", "iopub.status.busy": "2024-10-11T06:15:05.132759Z", "iopub.status.idle": "2024-10-11T06:15:05.612270Z", "shell.execute_reply": "2024-10-11T06:15:05.611443Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { 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"box": { "visible": true }, "line": { "color": "#c97a88" }, "marker": { "color": "#c97a88", "opacity": 0.5, "size": 8, "symbol": "cross" }, "meanline": { "visible": true }, "type": "violin" } ] }, "layout": { "annotationdefaults": { "font": { "size": 12 } }, "autotypenumbers": "strict", "hoverlabel": { "bgcolor": "white", "font": { "family": "Rockwell", "size": 14 } }, "hovermode": "x unified", "legend": { "bgcolor": "white", "bordercolor": "Black", "borderwidth": 1, "font": { "family": "Rockwell" } }, "xaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true }, "yaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true } } }, "width": 900, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ], "showticklabels": true, "title": { "text": "Time (s)" } }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ], "title": { "text": "Fractional Voltage" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the trace waveforms\n", "res_spectroscopy2D.plot_trace()" ] }, { "cell_type": "raw", "id": "e3bdac25", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "To view the output waveforms as a heatmap, you can use the following command. For more\n", "information on other available plot types refer to\n", ":py:meth:`~keysight.qcs.programs.Program.plot_iq`" ] }, { "cell_type": "code", "execution_count": 27, "id": "67264829", "metadata": { "execution": { "iopub.execute_input": "2024-10-11T06:15:05.666448Z", "iopub.status.busy": "2024-10-11T06:15:05.666047Z", "iopub.status.idle": "2024-10-11T06:15:05.847202Z", "shell.execute_reply": "2024-10-11T06:15:05.846494Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "hovertemplate": "readout_frequencies: %{x}

readout_pulse_amplitudes: %{y}
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"layout": { "annotationdefaults": { "font": { "size": 12 } }, "autotypenumbers": "strict", "hoverlabel": { "bgcolor": "white", "font": { "family": "Rockwell", "size": 14 } }, "hovermode": "x unified", "legend": { "bgcolor": "white", "bordercolor": "Black", "borderwidth": 1, "font": { "family": "Rockwell" } }, "xaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true }, "yaxis": { "linecolor": "black", "linewidth": 1, "mirror": true, "showline": true } } }, "width": 900, "xaxis": { "anchor": "y", "automargin": true, "domain": [ 0.0, 1.0 ], "nticks": 12, "showticklabels": true, "tickangle": 0, "title": { "text": "readout_frequencies" } }, "yaxis": { "anchor": "x", "automargin": true, "domain": [ 0.0, 1.0 ], "nticks": 12, "title": { "text": "readout_pulse_amplitudes" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the IQ data as a heatmap\n", "res_spectroscopy2D.plot_iq(plot_type=\"2d\")" ] }, { "cell_type": "raw", "id": "6dcb2dc9", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "This concludes the tutorial for running resonator spectroscopy experiments, both\n", "1D and 2D, using the Keysight QCS System. For detailed information on all available\n", "arguments and functionalities, please refer to the API documentation for\n", ":py:class:`~keysight.qcs.experiments.ResonatorSpectroscopy` and\n", ":py:class:`~keysight.qcs.experiments.ResonatorSpectroscopy2D`." ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "nbsphinx,raw_mimetype,-all", "main_language": "python", "notebook_metadata_filter": "-all", "text_representation": { "extension": ".py", "format_name": "percent" } }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }